Abstract
This work proposes a hybrid algorithm for the unrelated parallel machine scheduling problem with sequence-dependent setup times, aiming to minimize the makespan. The proposed algorithm, named Enhanced Smart General Variable Neighborhood Search (e-SGVNS), combines heuristic and exact optimization strategies to explore the solution space of the problem. The exact strategy works like a local search and consists of applying a mathematical programming formulation based on the time-dependent traveling salesman problem to obtain the optimal solution to the sequencing problem on each machine. In turn, the heuristic strategy explores neighborhoods based on swap and insertion moves. The computational results, performed in benchmark instances from literature, showed that e-SGVNS is competitive when compared to state-of-the-art algorithms.
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Acknowledgments
The authors gratefully thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001, Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG, grant PPM/CEX/FAPEMIG/676-17), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, grant 307915/2016-6), Universidade Federal de Ouro Preto (UFOP) and Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) for supporting this research. The authors also thank the anonymous reviewers for their valuable comments.
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Rego, M.F., Souza, M.J.F. (2020). A Hybrid Algorithm for the Unrelated Parallel Machine Scheduling Problem. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2019. Lecture Notes in Business Information Processing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-40783-4_3
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